Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Unveiling the Uncommon: Cushing's Syndrome (CS) Masquerading as Severe Hypokalemia.

Cushing's syndrome (CS) arises from an excess of endogenous or exogenous cortisol, with Cushing's disease specifically implicating a pituitary adenoma and exaggerated adrenocorticotropic hormone (ACTH) production. Typically, Cushing's disease presents with characteristic symptoms such as weight gain, central obesity, moon face, and buffalo hump. This case report presents an unusual manifestation of CS in a 48-year-old male with a history of hypertension, where severe hypokalemia was the primary presentation. Initial complaints included bilateral leg swelling, muscle weakness, occasional shortness of breath, and a general feeling of not feeling well. Subsequent investigations revealed hypokalemia, metabolic alkalosis, and an abnormal response to dexamethasone suppression, raising concerns about hypercortisolism. Further tests, including 24-hour urinary free cortisol and ACTH testing, confirmed significant elevations. Brain magnetic resonance imaging (MRI) identified a pituitary macroadenoma, necessitating neurosurgical intervention. This case underscores the rarity of CS presenting with severe hypokalemia, highlighting the diagnostic challenges and the crucial role of a collaborative approach in managing such intricate cases.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. RESULTS: In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth (BTG) Algorithm. Finally, the optimal feature space from the training dataset was fed to the Deep neural network (DNN) to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed an enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. AVAILABILITY: https://github.com/MateeullahKhan/DeepAVP-TPPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluating the Efficacy of Antioxidant Therapy in Enhancing the Quality of Life of Chronic Pancreatitis Patients: A Systematic Review.

Chronic pancreatitis (CP), an inflammatory disease characterized by irreversible pancreatic changes and progressive fibrosis, significantly impairs patients' quality of life. This systematic review aims to assess the efficacy of antioxidant therapy in enhancing the quality of life of CP patients. Focusing on the role of oxidative stress in CP pathogenesis, we explored several databases for studies evaluating the impact of antioxidant supplementation. The review included randomized controlled trials and cohort studies reporting pain frequency, intensity, and overall quality of life measures. Findings from these studies present a mixed view of the efficacy of antioxidants in CP, with some suggesting benefits in symptom management, while others show inconsistency in improving patient outcomes. The review concludes that while antioxidant therapy holds potential, especially in symptom alleviation, there is a need for more rigorous, larger-scale studies to confirm its effectiveness in CP management and to establish standardized treatment protocols. The incorporation of antioxidants into CP treatment plans should be approached with personalized care, considering the varied responses observed in different patient populations.

Comparative Probiogenomics Analysis of Limosilactobacillus fermentum 3872.

Limosilactobacillus fermentum is an important member of the lactic acid bacteria group and holds immense potential for probiotic properties in human health and relevant industries. In this study, a comparative probiogenomic approach was applied to analyze the genome sequence of L. fermentum 3872, which was extracted from a commercially available yogurt sample, along with 20 different publicly available strains. Results indicate that the genome size of the characterized L. fermentum 3892 strain is 2,057,839 bp, with a single- and circular-type chromosome possessing a G + C content of 51.69%. The genome of L. fermentum 3892 strain comprises a total of 2120 open reading frames (ORFs), two genes encoding rRNAs, and 53 genes encoding tRNAs. Upon comparative probiogenomic analysis, two plasmid sequences were detected among the study strains, including one for the L. fermentum 3872 genome, which was found between position 1,288,203 and 1,289,237 with an identity of 80.98. The whole-genome alignment revealed 2223 identical sites and a pairwise identity of 98.9%, indicating a significant difference of 1.1% among genome strains. Comparison of amino acid encoding genes among strains included in this study suggests that the strain 3872 exhibited the highest degree of amino acids present, including glutamine, glutamate, aspartate, asparagine, lysine, threonine, methionine, and cysteine. The comparative antibiotic resistome profiling revealed that strain 3872 exhibited a high resistant capacity only to ciprofloxacin antibiotics as compared to other strains. This study provides a genomic-based evaluation approach for comparative probiotic strain analysis in commercial foods and their significance to human health.

Multiwaves, breathers, lump and other solutions for the Heimburg model in biomembranes and nerves.

In this manuscript, a mathematical model known as the Heimburg model is investigated analytically to get the soliton solutions. Both biomembranes and nerves can be studied using this model. The cell membrane's lipid bilayer is regarded by the model as a substance that experiences phase transitions. It implies that the membrane responds to electrical disruptions in a nonlinear way. The importance of ionic conductance in nerve impulse propagation is shown by Heimburg's model. The dynamics of the electromechanical pulse in a nerve are analytically investigated using the Hirota Bilinear method. The various types of solitons are investigates, such as homoclinic breather waves, interaction via double exponents, lump waves, multi-wave, mixed type solutions, and periodic cross kink solutions. The electromechanical pulse's ensuing three-dimensional and contour shapes offer crucial insight into how nerves function and may one day be used in medicine and the biological sciences. Our grasp of soliton dynamics is improved by this research, which also opens up new directions for biomedical investigation and medical developments. A few 3D and contour profiles have also been created for new solutions, and interaction behaviors have also been shown.

Extracorporeal Shockwave for Intermittent Claudication and Quality of Life: A Randomized Clinical Trial.

Importance: Intermittent lower limb claudication limits function and quality of life. Supervised exercise programs are not readily available, and a noninvasive alternative is needed. Objective: To assess extracorporeal corporeal shockwave therapy in improving quality of life in patients with claudication. Design, Setting, and Participants: In this double-blind, placebo-controlled randomized clinical trial, patients in the outpatient setting at a single tertiary center for vascular surgery were randomized in a 1:1 ratio to extracorporeal shockwave therapy or placebo therapy with no shockwaves delivered. Recruitment was between June 2015 and January 2020, with 12-week follow-up ending in March 2020. A convenience sample of patients with claudication and conservative treatment requirements who refused or were unable to participate in supervised exercise were eligible. Patients receiving anticoagulation therapy or with an active cancer were excluded. Of 522 patients screened, 389 were eligible, 138 were enrolled, and 110 completed follow-up and were included in the primary analysis. Statistical analysis was completed by May 2021. Intervention: In the intervention group, patients received 100 impulses of 0.1mJ/mm/cm2 in an area of the gastrocnemius muscle 3 times weekly for 3 weeks. The steps for treatment were replicated for the control group without delivering the treatment. Main Outcomes and Measures: The primary outcome was the Physical Functioning domain of the 36-item Short-Form Quality of Life Questionnaire at 12-week follow-up. Secondary outcomes included walking distances, ankle brachial pressure index, and other quality-of-life measures. Results: Of 138 patients recruited and randomized, 92 (67%) were male, and the mean (SD) age of the study population was 67 (9.6) years. The intervention group had a significantly higher physical function score at 12 weeks (estimated median difference 3.8; 95% CI, 0.0-7.7; P = .03). However, this significance did not remain when adjusting for covariates. At 12 weeks, the intervention group had significantly longer pain-free and maximum walking distances (pain-free estimated median difference, 34.1, 95% CI, 11.4-56.8; P = .004; maximum estimated median difference, 51.4; 95% CI, 10.7-86.5; P = .01). Conclusions and Relevance: To our knowledge, this is the first double-blind, placebo-controlled, randomized clinical trial to consider extracorporeal shockwave therapy for the management of intermittent claudication. It demonstrated efficacy for walking distances, may have a positive effect on quality of life, and may provide a safe, noninvasive alternative therapy for patients with intermittent claudication. Trial Registration: ClinicalTrials.gov Identifier: NCT02652078.

Correction: Facile fabrication of a graphene-based chemical sensor with ultrasensitivity for nitrobenzene.

[This corrects the article DOI: 10.1039/D3RA08794H.].

Fractional epidemic model of coronavirus disease with vaccination and crowding effects.

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh-Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if R 0 < 1 and endemic equilibrium is locally asymptotically stable if R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if R 0 < 1 and endemic equilibrium is globally asymptotically stable if R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.

Molecular functions of microRNAs in colorectal cancer: recent roles in proliferation, angiogenesis, apoptosis, and chemoresistance.

MiRNAs (microRNAs) constitute a group of diminutive molecules of non-coding RNA intricately involved in regulating gene expression. This regulation is primarily accomplished through the binding of miRNAs to complementary sequences situated in the 3'-UTR of the messenger RNA (mRNA) target; as a result, they are degraded or repressed. The multifaceted biogenesis of miRNAs is characterized by a meticulously orchestrated sequence of events encompassing transcription, processing, transportation, and decay. Colorectal cancer stands as a pervasive and formidable ailment, afflicting millions across the globe. Colorectal cancer is not well diagnosed early, and metastasis rates are high, which results in low survival rates in advanced stages. The genesis and progression of colorectal cancer are subject to the influence of genetic and epigenetic factors, among which miRNAs play a pivotal role. When it comes to colorectal cancer, miRNAs have a dual character, depending on the genes they target, functioning as either tumor suppressors or oncogenes and the prevailing cellular milieu. Their impact extends to modulating critical facets of colorectal cancer pathogenesis, including proliferation, angiogenesis, apoptosis, chemoresistance, and radiotherapy response. The discernible potential of miRNAs which are used as biomarkers to diagnose colorectal cancer, prognosis, and treatment response has come to the forefront. Notably, miRNAs are easily found and detected readily in a variety of biological fluids, including saliva, blood, urine, and feces. This prominence is attributed to the inherent advantages of miRNAs over conventional biomarkers, including heightened stability, specificity, sensitivity, and accessibility. Various investigations have pinpointed miRNA signatures or panels capable of differentiating colorectal cancer patients from their healthy counterparts, predicting colorectal cancer stage and survival, and monitoring colorectal cancer recurrence and therapy response. Although there has been research on miRNAs in various diseases, there has been less research on miRNAs in cancer. Moreover, updated results of preclinical and clinical studies on miRNA biomarkers and drugs are required. Nevertheless, the integration of miRNAs as biomarkers for colorectal cancer is not devoid of challenges and limitations. These encompass the heterogeneity prevalent among colorectal cancer subtypes and stages, the variability in miRNA expression across different tissues and individuals, the absence of standardized methodologies for miRNA detection and quantification, and the imperative for validation through extensive clinical trials. Consequently, further research is imperative to conclusively establish the clinical utility and reliability of miRNAs as colorectal cancer biomarkers. MiR-21 demonstrates carcinogenic characteristics by targeting several tumor suppressor genes, which encourages cell division, invasion, and metastasis. On the other hand, by controlling the Wnt/ß-catenin pathway, the tumor suppressor miRNA miR-34a prevents CRC cell proliferation, migration, and invasion. Furthermore, in colorectal cancer, the miR-200 family increases chemotherapy sensitivity while suppressing epithelial-mesenchymal transition (EMT). As an oncogene, the miR-17-92 cluster targets elements of the TGF-ß signaling pathway to encourage the growth of CRC cells. Finally, miR-143/145, which is downregulated in CRC, influences apoptosis and the progression of the cell cycle. These miRNAs affect pathways like Wnt, TGF-ß, PI3K-AKT, MAPK, and EMT, making them potential clinical biomarkers and therapeutic targets. This review summarizes recent research related to miRNAs, their role in tumor progression and metastasis, and their potential as biomarkers and therapeutic targets in colorectal cancer. In addition, we combined miRNAs' roles in tumorigenesis and development with the therapy of CRC patients, leading to novel perspectives on colorectal cancer diagnosis and treatment.

Heart failure survival prediction using novel transfer learning based probabilistic features.

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

Detection of haptoglobin and serum amyloid A as biomarkers in naturally infected Mycoplasma bovis calves.

The livestock sector of Pakistan is increasing rapidly and it plays important role both for rural community and national economy. It is estimated that almost 8 million rural people are involved in livestock rearing and earning about 35-40 % of their income from the livestock sector. Mycoplasma bovis (M. bovis) infection causes significant economic losses in dairy animals especially young calf in the form of clinical illnesses such as pneumonia, poly-arthritis, respiratory distress and mortality. M. bovis is hard to diagnose and control because of uneven disease appearance and it is usually noticed in asymptomatic animals. For the identification of M. bovis in sub-clinical and clinical samples, determination of acute phase proteins i.e., haptoglobin (Hp) and serum amyloid A (SAA) are important tools for the timely diagnosis of disease. Therefore, early diagnosis of disease and hemato-biochemical changes are considered beneficial tools to control the infectious agent to uplift the economy of the dairy farmers. For this purpose, blood samples were collected from 200 calves of Bovidae family. Serum was separated from blood samples to determine the concentration of Hp and SAA, while blood samples were processed to determine hematological changes in blood from calves by using hematological analyzer. The blood plasma obtained from the blood samples was processed to measure oxidative stress factors. Lungs tissues from slaughterhouses/ morbid calves were collected to observe histopathological changes. The results of present study indicated that level of SAA and Hp remarkably increased (P < 0.05) in M. bovis infected calves in comparison to healthy calves. The oxidative stress markers indicated that nitric oxide and MDA levels in the infected calves increased significantly (P < 0.05), while infected claves had considerably lower levels of superoxide dismutase, catalase and glutathione. These findings indicate that oxidative stress play role to increase the level of APPs, while monitoring of APPs levels may serve as a valuable addition to the clinical evaluation of naturally infected calves with M. bovis. The hematological parameters were decreased significantly (P < 0.05). Altogether, this study suggests that Hp and SAA are proposed as promising biomarkers for detecting naturally occurring M. bovis infection in calves.

Macrophage-enriched novel functional long noncoding RNAs LRRC75A-AS1 and GAPLINC regulate polarization and innate immune responses.

INTRODUCTION: Macrophages (Mφs) are functionally dynamic immune cells that bridge innate and adaptive immune responses; however, the underlying epigenetic mechanisms that control Mφ plasticity and innate immune functions are not well elucidated. OBJECTIVE: To identify novel functions of macrophage-enriched lncRNAs in regulating polarization and innate immune responses. METHODS: Total RNA isolated from differentiating monocyte-derived M1 and M2 Mφs was profiled for lncRNAs expression using RNAseq. Impact of LRRC75A-AS1, GAPLINC and AL139099.5 knockdown was examined on macrophage differentiation, polarization markers, phagocytosis, and antigen processing by flow cytometry and florescence microscopy. Cytokine profiles were examined by multiplex bead array and cytoskeletal signaling pathway genes were quantified by PCR-based array. Gingival biopsies were collected from periodontally healthy and diseased subjects to examine lncRNAs, M1/M2 marker expression. RESULTS: Transcriptome profiling of M1 and M2 Mφs identified thousands of differentially expressed known and novel lncRNAs. We characterized three Mφ-enriched lncRNAs LRRC75A-AS1, GAPLINC and AL139099.5 in polarization and innate immunity. Knockdown of LRRC75A-AS1 and GAPLINC downregulated the Mφ differentiation markers and skewed Mφ polarization by decreasing M1 markers without a significant impact on M2 markers. LRRC75A-AS1 and GAPLINC knockdown also attenuated bacterial phagocytosis, antigen processing and inflammatory cytokine secretion in Mφs, supporting their functional role in potentiating innate immune functions. Mechanistically, LRRC75A-AS1 and GAPLINC knockdown impaired Mφ migration by downregulating the expression of multiple cytoskeletal signaling pathways suggesting their critical role in regulating Mφ migration. Finally, we showed that LRRC75A-AS1 and GAPLINC were upregulated in periodontitis and their expression correlates with higher M1 markers suggesting their role in macrophage polarization in vivo. CONCLUSION: Our results show that polarized Mφs acquire a unique lncRNA repertoire and identified many previously unknown lncRNA sequences. LRRC75A-AS1 and GAPLINC, which are induced in periodontitis, regulate Mφ polarization and innate immune functions supporting their critical role in inflammation.

Synergistic effects of hybrid microfibers on mechanical, thermal, and microstructural characterization of nanocomposites.

The use of geopolymers (GP) in cementitious composites provides a solution to reduce the significant carbon emissions associated with conventional cement production, thereby advancing environmentally friendly concrete construction practices. The promise of hybrid fiber-reinforced fly ash (FA)-based GP (HFGP) composites that combine microfibers and nanoparticles has not yet been fully comprehended. This research aims to enhance the mechanical and microstructural properties of HFGP blends by varying the proportion of nano calcium carbonate ( n - C a C O 3 ). The production of HFGP involved the use of two types of fibers: 1% carbon fibers and 0.5% basalt fibers. To achieve HFGP blends with a consistent fiber ratio, we incorporated four different levels of n - C a C O 3 , comprising 1%, 2%, 3%, and 4% of the mixture. The analysis of fractured samples encompassed microstructural and mineralogical characterization, which was conducted using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analysis. The results unveiled that the HFGP blend containing 3% n - C a C O 3 exhibited the highest levels of hardness, compressive strength, toughness modulus, and flexural strength while the use of 2% n - C a C O 3 produced the highest results for fracture toughness and impact strength. SEM analysis illustrated that n - C a C O 3 had a significant positive impact on the microstructure of GP. A considerable rise in hump intensity between 20 and 40 °C ( 2 θ ) was also seen in the XRD examination, indicating that calcium silicate hydrate (CSH) had formed after the primary binder, such as sodium aluminosilicate hydrate (NASH), had been present. The stretching of O-H bonds in water molecules was also seen in the HFGP spectra at 3399, 3436, 3436, and 3438 cm-1. Due to the higher water content in the HFGP network, which may influence the material's strength, these bands were more apparent and larger in specimens with additions of nanoparticles and hybrid fibers.

Facile fabrication of a graphene-based chemical sensor with ultrasensitivity for nitrobenzene.

Chemical sensors have a wide range of applications in a variety of industries, particularly for sensing volatile organic compounds. This work demonstrates the fabrication of a chemical sensor based on graphene deposited on Cu foils using low-pressure chemical vapor deposition, following its transfer on oxidized silicon through a wet etching method. Scanning electron microscopy, Raman spectroscopy and UV-vis spectroscopy of the transferred graphene were performed. A device was fabricated by simply connecting the strips of a Cu tape along the two opposite edges of graphene, which acted as a chemical sensor. The sensor was exposed to different analytes, namely acetone, propanol, benzyl chloride, nitrobenzene, carbon tetrachloride and acetic acid. A relative change in the resistance of the device was observed, which was attributed to the interaction of analytes with graphene as it changes charge concentrations in the graphene lattice. The fabricated sensor showed a notable sensitivity and response time for all analytes, particularly a sensitivity as high as 231.1 for nitrobenzene and a response time as short as 6.9 s for benzyl chloride. The sensor was also tested for analyte leakage from containers for domestic, laboratory and industrial applications.

iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming, and may have adverse side effects on normal cells. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction scheme called iAFPs-Mv-BiTCN to predict antifungal peptides correctly. The training peptides are encoded using word embedding methods such as skip-gram and attention mechanism-based bidirectional encoder representation using transformer. Additionally, transform-based evolutionary features are generated using the Pseduo position-specific scoring matrix using discrete wavelet transform (PsePSSM-DWT). The fused vector of word embedding and evolutionary descriptors is formed to compensate for the limitations of single encoding methods. A Shapley Additive exPlanations (SHAP) based global interpolation approach is applied to reduce training costs by choosing the optimal feature set. The selected feature set is trained using a bi-directional temporal convolutional network (BiTCN). The proposed iAFPs-Mv-BiTCN model achieved a predictive accuracy of 98.15 % and an AUC of 0.99 using training samples. In the case of the independent samples, our model obtained an accuracy of 94.11 % and an AUC of 0.98. Our iAFPs-Mv-BiTCN model outperformed existing models with a ~4 % and ~5 % higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed iAFPs-Mv-BiTCN model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

Surgical Interventions in Chronic Pancreatitis: A Systematic Review of Their Impact on Quality of Life.

This systematic review evaluates the efficacy of surgical interventions in improving the quality of life for patients with chronic pancreatitis (CP). A thorough literature search, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, identified 11 studies that focused on patient-reported outcomes after surgical treatments, including pancreatic resections, drainage procedures, and duodenum-preserving head resections. The findings indicate that organ-preserving procedures, notably the Frey and Beger operations, significantly enhance pain control and overall quality of life while reducing analgesic dependency. This review provides crucial insights into the long-term efficacy and comparative benefits of different surgical approaches, highlighting the need for personalized surgical strategies in CP management. It emphasizes the necessity for standardized outcome measures and further comparative research to refine CP treatment protocols.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.